» Articles » PMID: 36245131

Inclusion of Nonrandomized Studies of Interventions in Systematic Reviews of Interventions: Updated Guidance from the Agency for Health Care Research and Quality Effective Health Care Program

Abstract

Objectives: We developed guidance to inform decisions regarding the inclusion of nonrandomized studies of interventions (NRSIs) in systematic reviews (SRs) of the effects of interventions.

Study Design And Setting: The guidance workgroup comprised SR experts and used an informal consensus generation method.

Results: Instead of recommending NRSI inclusion only if randomized controlled trials (RCTs) are insufficient to address the SR key question, different topics may require different decisions regarding NRSI inclusion. We identified important considerations to inform such decisions from topic refinement through protocol development. During topic scoping and refinement, considerations were related to the clinical decisional dilemma, adequacy of RCTs to address the key questions, risk of bias in NRSIs, and the extent to which NRSIs are likely to complement RCTs. When NRSIs are included, during SR team formation, familiarity with topic-specific data sources and advanced analytic methods for NRSIs should be considered. During protocol development, the decision regarding NRSI inclusion or exclusion should be justified, and potential implications explained. When NRSIs are included, the protocol should describe the processes for synthesizing evidence from RCTs and NRSIs and determining the overall strength of evidence.

Conclusion: We identified specific considerations for decisions regarding NRSI inclusion in SRs and highlight the importance of flexibility and transparency.

Citing Articles

Quality Assessment of Cohort Studies in Complementary and Alternative Medicine: A Scoping Review Over Two Decades.

Norouzi M, Haghdoost A Iran J Public Health. 2025; 54(1):74-87.

PMID: 39902362 PMC: 11787836. DOI: 10.18502/ijph.v54i1.17576.


Integration of non-randomized studies with randomized controlled trials in meta-analyses of clinical studies: a meta-epidemiological study on effect estimation of interventions.

Mei F, Yao M, Wang Y, Huan J, Ma Y, Li G BMC Med. 2024; 22(1):571.

PMID: 39623370 PMC: 11613474. DOI: 10.1186/s12916-024-03778-1.


Oncoming Revolution in the Next Generation of Cohort Studies.

Moameri H, Norouzi M, Haghdoost A, Hosseini Golkar M Iran J Public Health. 2024; 53(11):2595-2599.

PMID: 39619915 PMC: 11607157. DOI: 10.18502/ijph.v53i11.16963.


The reporting quality of meta-epidemiological studies needs substantial improvement: a research on research study.

Long Y, Zheng Y, Wang X, Guo Q, Zhang N, Deng Y Syst Rev. 2024; 13(1):244.

PMID: 39342302 PMC: 11438193. DOI: 10.1186/s13643-024-02661-7.


Integrating randomized controlled trials and non-randomized studies of interventions to assess the effect of rare events: a Bayesian re-analysis of two meta-analyses.

Zhou Y, Yao M, Mei F, Ma Y, Huan J, Zou K BMC Med Res Methodol. 2024; 24(1):219.

PMID: 39333867 PMC: 11430109. DOI: 10.1186/s12874-024-02347-7.


References
1.
Shepshelovich D, Yahav D, Ben Ami R, Goldvaser H, Tau N . Concordance between the results of randomized and non-randomized interventional clinical trials assessing the efficacy of drugs for COVID-19: a cross-sectional study. J Antimicrob Chemother. 2021; 76(9):2415-2418. PMC: 8244730. DOI: 10.1093/jac/dkab163. View

2.
Hozo I, Djulbegovic B, Parish A, Ioannidis J . Identification of threshold for large (dramatic) effects that would obviate randomized trials is not possible. J Clin Epidemiol. 2022; 145:101-111. PMC: 9232885. DOI: 10.1016/j.jclinepi.2022.01.016. View

3.
Brockelmann N, Balduzzi S, Harms L, Beyerbach J, Petropoulou M, Kubiak C . Evaluating agreement between bodies of evidence from randomized controlled trials and cohort studies in medical research: a meta-epidemiological study. BMC Med. 2022; 20(1):174. PMC: 9092682. DOI: 10.1186/s12916-022-02369-2. View

4.
Reeves B, Wells G, Waddington H . Quasi-experimental study designs series-paper 5: a checklist for classifying studies evaluating the effects on health interventions-a taxonomy without labels. J Clin Epidemiol. 2017; 89:30-42. PMC: 5669452. DOI: 10.1016/j.jclinepi.2017.02.016. View

5.
Franklin J, Glynn R, Martin D, Schneeweiss S . Evaluating the Use of Nonrandomized Real-World Data Analyses for Regulatory Decision Making. Clin Pharmacol Ther. 2019; 105(4):867-877. DOI: 10.1002/cpt.1351. View